soil_af: Soil data for Africa

View source: R/soil_afsis.R

soil_afR Documentation

Soil data for Africa


Download chemical soil properties data for Africa for different soil depths. The spatial resolution is 30 arc-seconds (about 1 km2), aggregated from the original 250m resolution.

There are more recent estimations for some of the properties available in other data sets. See soil_af_isda and soil_world.

For more info, see

The data have a CC-BY 4.0 NC license


soil_af(var, depth, path, ...)



character. Variables name such as "pH" or "clay". See Details


numeric. One of 5, 15, 30, 60, 100, 200. This is shorthand for the following depth ranges: 0-5, 5-15, 15-30, 30-60, 60-100, 100-200 cm. Or one of 20, 50 for 0-20 or 20-50 cm


character. Path to download the data to


additional arguments passed to download.file


var description unit
clay Soil texture fraction clay %
sand Soil texture fraction sand %
silt Soil texture fraction silt %
coarse Coarse fragments volumetric %
SOC Organic carbon g kg-1 (‰)
BLKD Bulk density (fine earth) kg m-3
poros Porosity (volum. fraction) based on PTF -
AWpF2.0 Avail. soil water capacity (volum. frac.) for FC = pF 2.0 -
AWpF2.3 Avail. soil water capacity (volum. frac.) for FC = pF 2.3 -
AWpF2.5 Avail. soil water capacity (volum. fract.) for FC = pF 2.4 -
AWpF4.2 Avail. soil water capacity (volum. fract.) at wilting point (pF 4.2) -
BDR Depth to bedrock cm
. . .
pH pH (H2O) -
ECN Electrical conductivity mS/m (?)
acid-exch Exchangeable acidity cmol(+) kg-1
bases-exch Sum of exchangeable bases cmol(+) kg-1
CEC Cation Exchange Capacity cmol(+) kg-1
Al-extr Extractable Aluminum (Mehlich 3) mg kg-1 (ppm)
Al-exch Exchangeable Aluminum cmol(+) kg-1
Ca-exch Exchangeable Calcium cmol(+) kg-1
K-exch Exchangeable Potassium cmol(+) kg-1
Mg-exch Exchangeable Magnesium cmol(+) kg-1
Na-exch Exchangeable Sodium cmol(+) kg-1
Ntot Total nitrogen g kg-1




Hengl T, Heuvelink GBM, Kempen B, Leenaars JGB, Walsh MG, Shepherd KD, et al. (2015) Mapping Soil Properties of Africa at 250 m Resolution: Random Forests Significantly Improve Current Predictions. PLoS ONE 10(6): e0125814. doi:10.1371/journal.pone.0125814

See Also

soil_af_elements, soil_af_isda, soil_world_vsi


aph <- soil_af(var="ph", depth=5, path=tempdir())

geodata documentation built on Dec. 1, 2022, 5:12 p.m.